wrProteo: testRobustToNAimputation – R documentation

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testRobustToNAimputation

Pair-wise testing robust to NA-imputation

Description

testRobustToNAimputation replaces NA values based on group neighbours (based on grouping of columns in argument gr), following overall assumption of close to Gaussian distribution. Furthermore, it is assumed that NA-values originate from experimental settings where measurements at or below detection limit are recoreded as NA. In such cases (eg in proteomics) it is current practice to replace NA-values by very low (random) values in order to be able to perform t-tests. However, random normal values used for replacing may in rare cases deviate from the average (the 'assumed' value) and in particular, if multiple NA replacements are above the average, may look like induced biological data and be misinterpreted as so. The statistical testing uses eBayes from Bioconductor package limma for robust testing in the context of small numbers of replicates. By repeating multiple times the process of replacing NA-values and subsequent testing the results can be sumarized afterwards by median over all repeated runs to remmove the stochastic effect of individual NA-imputation. Thus, one may gain stability towards random-character of NA imputations by repeating imputation & test 'nLoop' times and summarize p-values by median (results stabilized at 50-100 rounds). It is necessary to define all groups of replicates in gr to obtain all possible pair-wise testing (multiple columns in $BH, $lfdr etc). The modified testing-procedure of Bioconductor package ROTS may optionaly be included, if desired. This function returns a limma-like S3 list-object further enriched by additional fields/elements.

Usage

testRobustToNAimputation(
  dat,
  gr,
  annot = NULL,
  retnNA = TRUE,
  avSdH = c(0.15, 0.5),
  avSdL = NULL,
  plotHist = FALSE,
  xLab = NULL,
  tit = NULL,
  seedNo = NULL,
  multCorMeth = NULL,
  nLoop = 100,
  lfdrInclude = NULL,
  ROTSn = NULL,
  silent = FALSE,
  callFrom = NULL
)

Arguments

`dat`	(matrix or data.frame) main data (may contain `NA`); if `dat` is list containing $quant and $annot as matrix, the element $quant will be used
`gr`	(character or factor) replicate association
`annot`	(matrix or data.frame) annotation (lines must match lines of data !), if `annot` is `NULL` and argument `dat` is a list containing both $quant and $annot, the element $annot will be used
`retnNA`	(logical) retain and report number of `NA`
`avSdH`	(numeric) population characteristics (mean and sd) for >1 `NA` neighbours 'high' (per line)
`avSdL`	depreciated argument, no longer used
`plotHist`	(logical) additional histogram of original, imputed and resultant distribution (made using `matrixNAneighbourImpute` )
`xLab`	(character) custom x-axis label
`tit`	(character) custom title
`seedNo`	(integer) seed-value for normal random values
`multCorMeth`	(character)
`nLoop`	(integer) number of runs of independent `NA`-imputation
`lfdrInclude`	(logical) depreciated, please used `multCorMeth` instead (include lfdr estimations, may cause warning message(s) concerning convergence if few too lines/proteins in dataset tested).
`ROTSn`	(integer) depreciated, please used `multCorMeth` instead (number of repeats by `ROTS`, if `NULL` `ROTS` will not be called)
`silent`	(logical) suppress messages
`callFrom`	(character) allows easier tracking of messages produced

Details

The argument multCorMeth allows to choose which multiple correction algorimths will be used and included to the final results. Possible options are 'lfdr','BH','BY','tValTab', ROTSn='100' (name to element necessary) or 'noLimma' (to add initial p.values and BH to limma-results). By default 'lfdr' (local false discovery rate from package 'fdrtools') and 'BH' (Benjamini-Hochberg FDR) are chosen. The option 'BY' referrs to Benjamini-Yakuteli FDR, 'tValTab' allows exporting all individual t-values from the repeated NA-substitution and subsequent testing.

Value

limma-type S3 object of class 'MArrayLM' which can be accessed; multiple results of testing or multiple testing correction types may get included ('p.value','FDR','BY','lfdr' or 'ROTS.BH')

Examples

set.seed(2015); rand1 <- round(runif(600) +rnorm(600,1,2),3)
dat1 <- matrix(rand1,ncol=6) + matrix(rep((1:100)/20,6),ncol=6)
dat1[13:16,1:3] <- dat1[13:16,1:3] +2    # augment lines 13:16 
dat1[19:20,1:3] <- dat1[19:20,1:3] +3    # augment lines 19:20
dat1[15:18,4:6] <- dat1[15:18,4:6] +1.4  # augment lines 15:18 
dat1[dat1 <1] <- NA                    # mimick some NAs for low abundance
## normalize data
boxplot(dat1, main="data before normalization")
dat1 <- wrMisc::normalizeThis(as.matrix(dat1), meth="median")
## designate replicate relationships in samples ...  
grp1 <- gl(2, 3, labels=LETTERS[1:2])                   
## moderated t-test with repeated inputations (may take >10 sec,  >60 sec if ROTSn >0 !) 
PLtestR1 <- testRobustToNAimputation(dat=dat1, gr=grp1, retnNA=TRUE, nLoop=70)
names(PLtestR1)

wrProteo

Proteomics Data Analysis Functions

v1.4.1

GPL-3

Authors

Wolfgang Raffelsberger [aut, cre]

Initial release